The paper proposes a wireless navigation mobile robot system for both path planning and trajectory execution within an indoor maze environment. This system consists of the mobile robot, trajectory planner, motion controller, visual sensor (CCD camera), ZigBee wireless communication device and a maze terrain. The camera is used to capture images of the mobile robot within the maze. Developed image processing and analyzing algorithms determine the robot's position and orientation based on color markers recognition. Markers are mounted on the top of the robot. Based on this data the implemented navigation system calculates a trajectory for the mobile robot from a starting point to a target point. The proposed navigation system is an upgrade to our previously developed system. Maze encryption and motion planning modules have been added to the previous system. Breadth First Search (BFS) and modified Depth First Search (DFS) algorithms were used for the trajectory calculation. A developed control algorithm calculates control signals in real time. These signals are sent to the robot via modules for wireless communication, causing robot motion along the calculated trajectory and eventually, the completion of the trajectory. The whole control system is realized and experimental results have been obtained. The experimental results confirm the robustness and effectiveness of the implemented control system.

This paper deals with the identification process of an ethane-ethylene distillation column system and introduces a procedure for MIMO system identification using Matlab's IDENT toolbox. An existing, five inputs and three outputs, ninety samples dataset has been analyzed. Four separate datasets are analysed, each having a different level of noise. As a part of the proposed procedure, both nonparametric and parametric identification methods have been implemented and results have been discussed. A comparative analysis of the results for all of the tested model structures has been carried out and the best performing model has been chosen. A Simulink model of the identified system has been implemented based on the best performing parametric model. It has been stimulated with the existing input datasets and the resulting output signals have been compared to the existing output datasets. The obtained results confirm the quality of the obtained model and affirm the correctness of the proposed and implemented procedure.

This paper deals with the design and implementation of a discrete dynamic control system using multiobjective dynamic programming. A discrete dynamic system along with the constraints regarding the values of the system's state (output) and the applicable control input has been introduced. Using multi-objective dynamic programming, a control system has been designed which minimizes two objectives while satisfying introduced constraints. The implemented control solution is explained. Experiments have been performed to test the quality of the implemented solution. A single-objective version of the control problem has been tested to provide reference for analysis regarding the quality of the implemented multi-objective control solution. The comparison of the obtained experimental results confirms the effectiveness of the proposed control solution and demonstrates the benefits of the multiobjective approach.

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